databutler-software-eol

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly/idempotent/non-destructive, and the description goes well beyond them: it discloses the data provenance (monthly endoflife.date snapshot, 340 release cycles, verified 2026-10-03) and the exact ambiguity behavior ('candidates' for multi-edition Windows, 'nearest matches' for unknown input). That is substantive behavioral context for a no-output-schema tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core purpose, then dense but useful resolution and return detail in two long sentences. Slightly heavy on parenthetical examples, but nearly every clause carries information an agent needs.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description correctly enumerates the return payload (supported flag, eol date, days remaining, active/extended support, LTS, latest release, recommendation, source URL) and covers edge cases. The one gap is that it never positions itself against support_timeline.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning by showing how a version string resolves to a cycle ('18.19.0' and 'v18' → Node 18; '1.8.0_392' → Java 8; '11-24h2-w' → Windows 11 24H2 consumer), which the schema does not explain.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific question ('whether a version of nodejs, python, ... is still supported') with an enumerated resource set and concrete resolution examples. It does not, however, differentiate itself from the sibling support_timeline, so an agent must infer the boundary between 'status now' and 'timeline'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the phrasing 'as of today', which suggests a point-in-time status check, but there is no explicit when-to-use, when-not-to-use, or named alternative (support_timeline). The alias and nearest-match notes help invocation but not tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish the safe read-only, idempotent, non-destructive profile, so the description's job is to add beyond that — and it does: the monthly snapshot provenance, 'as of today' staleness caveat for supported true|false, newest-first ordering, and the product page as source. It does not cover pagination or rate limits, but for a snapshot-backed read tool this is solid added context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one long, list-heavy sentence that front-loads the resource but then re-enumerates all 14 product names already present in the schema. That redundancy costs space without adding information, though the return-field inventory is useful and nothing is padded with filler prose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the burden of describing return values and does so thoroughly, listing release dates, EOL, LTS status, support windows, and the supported-cycle list. The one real gap is routing guidance relative to support_status, which keeps it from a 5.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the single parameter already documents the accepted values and aliases (node, java, postgres), all of which the description repeats. It adds no new syntax or format meaning beyond the schema, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a concrete resource (every release cycle of a named product) and enumerates the exact fields returned, so an agent knows precisely what comes back. It references the sibling support_status only to note a shared data source, not to differentiate the two tools' purposes. Clear, but sibling differentiation is weak.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no statement of when to call this versus support_status, which the description itself acknowledges exists. The mention of the shared monthly endoflife.date snapshot implies a relationship but leaves the selection decision entirely to inference. No prerequisites or exclusions are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.